UNDERSTANDING THE ADOPTION OF A MOBILE APPLICATION TO SUPPORT WORKFLOW OF HEALTHCARE AIDES
Bibliographic record
Abstract
Abstract Healthcare aides are unlicensed support personnel who provide direct care, personal assistance and support to persons living with health conditions. Workflow issues have a negative impact on health care aides’ job satisfaction and quality of care. The implementation of information communication technologies could improve workflow. In collaboration with an industry partner, we developed a mobile application intended to support the workflow of health care aides who provide services to long-term care residents living with dementia. The purpose of this study was to investigate the technology acceptance and usability of a mobile application in a real-world environment when used by health care aides of a care facility. We used a sequential explanatory mixed methods approach. Our study included pre and post paper-based questionnaires with no control group (n=60). This was followed by two focus groups with a subsample of health care aides informed by qualitative description (n=12). We found: (a) acceptance of the mobile application was high; (b) usefulness was the strongest predictor of intention to use the mobile application, and (c) intention to use the mobile application predicted usage behaviour. Focus group findings supported the quantitative findings and highlighted participants’ strong belief that the mobile application was useful, portable, and reliable. An area for improvement was user interface adjustments. Overall, these results support the assertion that our mobile application assisted health care aides in addressing their workflow issues and thus, has potential to improve the quality of care provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".